A package for sample size other parameters calculations in diagnostic tests.
Project description
SampleParaCal
A comprehensive Python package for calculating sample sizes in medical diagnostic studies. This package implements a wide range of statistical methods for diagnostic research, including:
- Sample size calculations for diagnostic accuracy studies
- ROC curve analysis
- Comparison of multiple diagnostic tests
- Non-inferiority and equivalence testing
- Multi-reader multi-case (MRMC) study design
Installation
pip install spcal
Quick Start
import spcal as spc
# Calculate sample size for a study using a two-sided confidence interval
n = spc.two_sided_CI_sample_size(var=0.25, alpha=0.05, L=0.1)
print(f"Required sample size: {n}")
# Calculate AUC variance
variance = spc.AUC_variance_binormal(A=0.85, R=1.5)
print(f"AUC variance: {variance:.6f}")
# Compare two diagnostic tests
n = spc.sample_size_for_two_diagnostic_tests(
alpha=0.05,
beta=0.2,
delta=0.1,
Se1=0.85,
Se2=0.75,
coPos=0.6
)
print(f"Sample size for comparison: {n}")
Features
Single Diagnostic Method Evaluation
- Confidence interval-based sample size calculations
- Area Under ROC Curve (AUC) variance estimation
- Partial AUC analysis
- High-accuracy test evaluation
- Clustered data analysis
Threshold Optimization
- Sensitivity calculation at fixed false positive rates
- Variance estimation for transformed sensitivity
- Binormal ROC curve modeling
Diagnostic Method Comparison
- Paired and unpaired sample size calculations
- Relative sensitivity and specificity comparisons
- Covariance estimation for correlated tests
- Predictive value comparison (PPV/NPV)
Non-inferiority and Equivalence Testing
- Non-inferiority sample size calculations
- Equivalence testing for diagnostic methods
- Clustered data equivalence testing
Multi-reader Studies
- Variance components for reader variability
- Sample size for multi-reader studies
- Multi-reader multi-case (MRMC) study design
License
This project is licensed under the MIT License - see the LICENSE file for details.
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